Studying the effectiveness of multi-user immersive environments for collaborative evaluation tasks

Massively Multiuser On-line Learning (MMOL) Platforms, often called “virtual learning worlds”, constitute a still unexplored context for communication-enhanced learning, where synchronous communication skills in an explicit social setting enhance the potential of effective collaboration. In this paper, we report on an experimental study of collaborative evaluation in an MMOL setting with 21 graduate students enrolled in university courses in technology-mediated teaching and learning. This study was carried out using a prototype of a 3D MMOL platform built around an interactive space called “MadriPolis”. This space was used to recreate an adequate scenario for a collaborative experience about Learning Object evaluation using the mainstream Learning Object Review Instrument (LORI), which is based on a Convergent Participation Model (CPM). The same experience was carried out using a conventional LCMS (Learning Content Management System) platform with the aim of contrasting the outcomes and interaction patterns in the two settings. This study makes use of Social Network Analysis (SNA) measures to describe the interactions between tutors and learners. By dwelling on the advantages of immersive environments, SNA indexes revealed that these interactions were rather dense and that student participation was rather broad-based in the case of the MMOL. The results suggest that MMOL platforms could be used in collaborative evaluation tasks as a means to enhance both tutor interaction patterns and the strength of the group's relationship.     

Studying the effectiveness of multi-user immersive environments for collaborative evaluation tasks

Massively Multiuser On-line Learning (MMOL) Platforms, often called “virtual learning worlds”, constitute a still unexplored context for communication-enhanced learning, where synchronous communication skills in an explicit social setting enhance the potential of effective collaboration. In this paper, we report on an experimental study of collaborative evaluation in an MMOL setting with 21 graduate students enrolled in university courses in technology-mediated teaching and learning. This study was carried out using a prototype of a 3D MMOL platform built around an interactive space called “MadriPolis”. This space was used to recreate an adequate scenario for a collaborative experience about Learning Object evaluation using the mainstream Learning Object Review Instrument (LORI), which is based on a Convergent Participation Model (CPM). The same experience was carried out using a conventional LCMS (Learning Content Management System) platform with the aim of contrasting the outcomes and interaction patterns in the two settings. This study makes use of Social Network Analysis (SNA) measures to describe the interactions between tutors and learners. By dwelling on the advantages of immersive environments, SNA indexes revealed that these interactions were rather dense and that student participation was rather broad-based in the case of the MMOL. The results suggest that MMOL platforms could be used in collaborative evaluation tasks as a means to enhance both tutor interaction patterns and the strength of the group's relationship.     

Smooth Gaze: a framework for recovering tasks across devices using eye tracking

A user task is often distributed across devices, e.g., a student listening to a lecture in a classroom while watching slides on a projected screen and making notes on her laptop, and sometimes checking Twitter for comments on her smartphone. In scenarios like this, users move between heterogeneous devices and have to deal with task resumption overhead from both physical and mental perspectives. To address this problem, we created Smooth Gaze, a framework for recording the user’s work state and resuming it seamlessly across devices by leveraging implicit gaze input. In particular, we propose two novel and intuitive techniques, smart watching and smart posting, for detecting which display and target region the user is looking at, and transferring and integrating content across devices respectively. In addition, we designed and implemented a cross-device reading system SmoothReading that captures content from secondary devices and generates annotations based on eye tracking, to be displayed on the primary device. We conducted a study that showed that the system supported information seeking and task resumption, and improved users’ overall reading experience.     

HMPMR strategy for real-time tracking in aerial images,using direct methods

The vast majority of approaches make use of features to track objects. In this paper, we address the tracking problem with a tracking-by-registration strategy based on direct methods. We propose a hierarchical strategy in terms of image resolution and number of parameters estimated in each resolution, that allows direct methods to be applied in demanding real-time visual-tracking applications. We have called this strategy the Hierarchical Multi-Parametric and Multi-Resolution strategy (HMPMR). The Inverse Composition Image Alignment Algorithm (ICIA) is used as an image registration technique and is extended to an HMPMR-ICIA. The proposed strategy is tested with different datasets and also with image data from real flight tests using an Unmanned Aerial Vehicle, where the requirements of direct methods are easily unsatisfied (e.g. vehicle vibrations). Results show that using an HMPMR approach, it is possible to cope with the efficiency problem and with the small motion constraint of direct methods, conducting the tracking task at real-time frame rates and obtaining a performance that is comparable to, or even better than, the one obtained with the other algorithms that were analyzed.     

Emotion recognition using facial expressions in an immersive virtual reality application

Virtual Reality - Facial expression recognition (FER) is an important method to study and distinguish human emotions. In the virtual reality (VR) context, people’s emotions are instantly and...     

Perception of cloth in assistive robotic manipulation tasks

Natural Computing - Assistive robots need to be able to perform a large number of tasks that imply some type of cloth manipulation. These tasks include domestic chores such as laundry handling or...     

Evaluation of eye,head and trunk coordination during target tracking tasks

This study tested the feasibility of a method to synchronise and to evaluate eye, head and trunk movement patterns during target tracking tasks performed by 10 subjects. A projected central target was randomly repositioned at 40° and 70° of rotation to the left and right. Subjects were instructed to change gaze as quickly as possible. Head and trunk motion was measured using a motion analysis system, and eye movement was measured using an eye-tracker; all data were synchronised. For healthy subjects, the eye moved faster than the head, there was no trunk movement and the head moved more than the eye to reach further displaced targets. The method tested was feasible, and it could be used to evaluate eye, head and trunk movement patterns of subjects with injuries such as whiplash and concussions.

Practitioner Summary: Studies of eye, head and trunk movements using synchronised methods are needed. We tested the feasibility of a method to synchronise and evaluate eye, head and trunk movement patterns. The method tested was feasible, and it could be used to evaluate movement patterns of subjects with injuries such as whiplash.     

Experience-based optimization of universal manipulation strategies for industrial assembly tasks

The trend towards smaller lot sizes and shorter product life cycles requires automation solutions with higher flexibility. Today’s robotic systems often are uneconomical for frequently changing boundary conditions and varying tasks due to high engineering costs needed for a well-defined supply of parts and pallets. At the same time, even small inaccuracies due to shape deviations in parts or pallets often cause high downtime. This work contributes to the robustness of industrial assembly processes with high inaccuracy concurrent to narrow tolerances. Therefore, contact-based manipulation strategies are defined, which are model-free and object-independent and solve common industrial tasks as palletizing, packaging and machine feeding. While the strategies are robust to inaccuracy up to 5 mm/5° due to localization uncertainty or object displacement, they handle usual industrial assembly tolerance of far below 1mm. The necessary flexibility and reusability for new tasks is guaranteed by hierarchical decomposition into atomic sub-strategies. In order to accelerate the execution, the manipulation strategies are customized to each specific task by unsupervised experience-based learning. The flexibility of the manipulation strategies and the progress in cycle time during the execution are shown on common industrial tasks with varying objects, tolerances and inaccuracies.     

The automatic creation of OLAP cube using an MDA approach

The Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation. To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.     

Distributed, multi-sensor tracking of multiple participants within immersive environments using a 2-cohort camera setup

Vision-based human motion tracking has gained significant interest in recent years, as the need for more intuitive human–computer interaction paradigms are sought after. Immersive environments, such as large-scale displays and virtual reality systems are particularly suitable for such interaction mechanisms, as they provide a controlled basis for experimentation and evaluation. However, despite the growing interest, human-motion tracking within immersive environments is still in its infancy. The lack of visible light and the presence of multiple people within such environments pose several challenges to the problem. In this research a robust framework for real-time, multi-camera human-motion tracking within an immersive environment is presented. The static nature of the environment is exploited to utilise a novel 2-cohort camera layout. This layout proves to be highly useful for tracking, providing several benefits over existing techniques, which treat all cameras as the same.     

Evaluation of complex and dynamic safety tasks in human learning using the ACT-R and SOAR skill acquisition theories

This paper provides a human-centered analytical approach to learning dynamic and complex tasks using the Adaptive Control of Thought-Rational (ACT-R) and the State, Operator And Result (SOAR) models by comparing the task times of the model and the subjects. Twenty-one full time assembly line workers at a local computer company (14 men and 7 women) from ages 18-32 (Mean = 19.86 years, SD = 0.96 years) were randomly selected for this analysis. The task involved the placement of printed circuit board (PCB) components on the flow line of the desktop computer mother board manufacturing process. The overall timed performance of the subjects indicated that the match between the model and the subjects was good, resulting in an R² - value of 0.94. At the unit task level performance, and R² - value of 0.96 for placing the PCBs on the flow line. For tasks involving picking and searching of PCBs, the obtained R² - value was 0.76 and R² of 0.68 at the keystroke level. Findings revealed that the model already started out with a complete strategy of performing the task, whereas the human participants had to acquire additional learning information during the trials. Efforts will be made in the future to determine how the performance of the human subjects could be enhanced to meet or the same level as the model performance.     

Main sequence: an index for detecting mental workload variation in complex tasks

The primary aim of this study was to validate the saccadic main sequence, in particular the peak velocity [PV], as an alternative psychophysiological measure of Mental Workload [MW]. Taking the Wickens’ multiple resource model as the theoretical framework of reference, an experiment was conducted using the Firechief^® microworld. MW was manipulated by changing the task complexity (between groups) and the amount of training (within groups). There were significant effects on PV from both factors. These results provide additional empirical support for the sensitivity of PV to discriminate MW variation on visual-dynamic complex tasks. These findings and other recent results on PV could provide important information for the development of a new vigilance screening tool for the prevention of accidents in several fields of applied ergonomics.     

Lean Cuisine+: an executable graphical notation for describing direct manipulation interfaces

The paper describes an executable semi-formal graphical notation, Lean Cuisine+, for describing the underlying behaviour of event-based direct manipulation interfaces, and outlines a methodology for constructing Lean Cuisine+ specifications. Lean Cuisine+ is a multilayered notation, and is a development of the meneme model of Lean Cuisine. A motivation of the research stems from the need for tools and techniques to facilitate high-level interface design. The research supports and brings together a number of views concerning the requirements of notations at this level. These are that a notation should be semi-formal, graphical, executable, and object-based, and that to be most effective it should be targeted at a specific category of interaction. The Lean Cuisine+ notation meets all these criteria, the underlying meneme model matching closely with the selection-based nature of direct manipulation interfaces.     

A new indexing method for complex similarity queries in immersive multimedia systems

This paper proposes a novel indexing method for complex similarity queries in high-dimensional image and video systems. In order to provide the indexing method with the flexibility in dealing with multiple features and multiple query objects, we treat every dimension independently. The efficiency of our method is realized by a specialized bitmap indexing that represents all objects in a database as a set of bitmaps. The percentage of data accessed in our indexing method is inversely proportional to the overall dimensionality, and thus the performance deterioration with the increasing dimensionality does not occur. To demonstrate the efficacy of our method we conducted extensive experiments and compared the performance with the VA-file-based index and the linear scan by using real image and video datasets, and obtained a remarkable speed-up over them.     

Review of graphical notations for specifying direct manipulation interfaces

At the early stage of interface design, the designer requires models and notations that can assist with reasoning and the exploration of ideas, and which are unconstrained by implementation issues. The review focuses on graphical notations for specifying the underlying behaviour of direct manipulation interfaces. Parallels are drawn with the requirements of notations employed in the more general areas of system requirements specifications. A number of existing graphical notations are compared and contrasted through a common example drawn from the Macintosh interface, and the required scope of an ideal notation for describing the behaviour of direct manipulation interfaces is defined.     

Nonlinear tracking control of trailer systems using the Lyapunov direct method

This article concerns the nonlinear tracking control of trailer systems that consist of a steering tractor and a passive trailer, linked together with rigid joint and having nonholonomic velocity constraints. We propose a globally asymptotically stable (GAS) tracking control law using the Lyapunov direct method. The GAS tracking control law is completed by considering the posture term of the trailer systems. We selected two types of trailer systems for examples: the two‐wheeled trailer system and the car‐like trailer system. The simulation results represent the effectiveness of our proposed method. In addition, there is a plan to test the path following ability of the trailer system. ©1999 John Wiley & Sons, Inc.     

Evaluation of the impact of different levels of self-representation and body tracking on the sense of presence and embodiment in immersive VR

Virtual Reality - The main goal of this paper is to investigate the effect of different types of self-representations through floating members (hands vs. hands + feet), virtual...     

Measuring flow experience in an immersive virtual environment for collaborative learning

In contexts other than immersive virtual environments, theoretical and empirical work has identified flow experience as a major factor in learning and human–computer interaction. Flow is defined as a ‘holistic sensation that people feel when they act with total involvement’. We applied the concept of flow to modeling the experience of collaborative learning in an immersive virtual environment. The aims were, first, to psychometrically evaluate a measurement model of flow and, second, to test a structural model of flow. Pairs of small teams engaged in collaborative problem‐solving tasks while communicating by way of an immersive virtual environment. Flow was measured after each session, using Guo and Poole's inventory for measuring flow in human–computer interaction. In relation to the first aim, partial‐least‐squares analysis demonstrated strong evidence for the measurement model. In relation to the second aim, the structural model was supported: the effect of learning‐task characteristics on flow experience was mediated by its precursors, with extraneous variables held constant. It is reasoned that the experiment and resultant analysis of this work contributes to the development of measurement models and structural models of flow in immersive virtual environments.